I started with the skewed distributions angle (income, housing prices) which is the obvious answer, and they nodded along.
Start by defining mean and median and their respective strengths. Then, explain that the median is preferred when data is skewed or has outliers, as it provides a more robust measure of central tendency. Finally, illustrate with a concrete example relevant to software engineering or product analytics.
Pro tip: Mention that the median is often used in A/B testing for metrics like response times or revenue per user, where outliers can distort the mean and lead to incorrect conclusions.
Briefly define mean and median, highlighting that mean is sensitive to extreme values while median is resistant.
Explain situations such as skewed distributions, presence of outliers, or when data is ordinal. Emphasize robustness.
Give concrete examples, e.g., household income, website load times, or user session durations, where median is more representative.
Connect to software engineering and McKinsey context: e.g., analyzing performance metrics, customer data, or A/B test results where outliers are common.
Conclude that while median is robust, mean is useful for further statistical analysis; choice depends on data distribution and goal.
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